Identification of Core Predication-Related Candidate Genes in Ovarian Cancer Based on Integrated Bioinformatics and Experienment

Abstract

Background: Ovarian cancer is one of the deadliest and most common gynecological malignancies. This study aims to use comprehensive bioinformatics analysis to try to identify the core candidate genes related to the prediction of ovarian cancer for the early diagnosis and prognosis of ovarian cancer. Methods: Obtain expression profiles from Gene Expression Omnibus database, identify differentially expressed genes (DEG) with p<0.05 and (logFC)>1.5, perform functional enrichment, protein-protein interaction (PPI) network construction, functional module analysis, and survival analysis And correlation analysis to obtain the target gene, through immunohistochemical staining, clinicopathological feature analysis to verify the expression and clinical significance of TTK.Results: 1. Identified 135 genes with the same expression. 33 up-regulated DEG were mainly enriched in mitotic spindle assembly checkpoints, chromosome segregation regulation, etc.; 102 down-regulated DEG was mainly enriched in neurotransmitter level regulation, protein serine/threonine Regulation of acid kinase activity, etc. Then the PPI network was constructed to screen 20 hub genes and perform survival analysis and expression correlation analysis. At the same time, the modules that met the requirements were screened and the genes were analyzed by pathway enrichment. It was found that TTK was highly expressed in ovarian cancer and led to a poor prognosis.2. Distant metastasis, lymph node metastasis, clinical staging (stage III-IV), and poor differentiation are independent risk factors for high TTK expression (P<0.05).3. TTK, CA125, HE4 three biological indicators show excellent diagnostic value in joint monitoring of ovarian cancer.Conclusions: TTK plays a vital role in the tumorigenesis, aggressiveness and malignant biological behavior of EOC, and can be used as a potential biomarker and potential therapeutic target for early diagnosis and predictive evaluation of EOC.